AIMC Topic: Skeleton

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Skeleton-Based Abnormal Behavior Detection Using Secure Partitioned Convolutional Neural Network Model.

IEEE journal of biomedical and health informatics
Theabnormal behavior detection is the vital for evaluation of daily-life health status of the patient with cognitive impairment. Previous studies about abnormal behavior detection indicate that convolution neural network (CNN)-based computer vision o...

Self-Supervised Action Representation Learning Based on Asymmetric Skeleton Data Augmentation.

Sensors (Basel, Switzerland)
Contrastive learning has received increasing attention in the field of skeleton-based action representations in recent years. Most contrastive learning methods use simple augmentation strategies to construct pairs of positive samples. When using such...

Fine-Grained Unsupervised Temporal Action Segmentation and Distributed Representation for Skeleton-Based Human Motion Analysis.

IEEE transactions on cybernetics
Understanding the fine-grained temporal structure of human actions and its semantic interpretation is beneficial to many real-world tasks, such as sports movements, rehabilitation exercises, and daily-life activities analysis. Current action segmenta...

Skeleton-Based Human Pose Recognition Using Channel State Information: A Survey.

Sensors (Basel, Switzerland)
With the increasing demand for human-computer interaction and health monitoring, human behavior recognition with device-free patterns has attracted extensive attention. The fluctuations of the Wi-Fi signal caused by human actions in a Wi-Fi coverage ...

A Bioinspired Fluid-Filled Soft Linear Actuator.

Soft robotics
In bioinspired soft robotics, very few studies have focused on fluidic transmissions and there is an urgent need for translating fluidic concepts into realizable fluidic components to be applied in different fields. Nature has often offered an inspir...

Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition.

Sensors (Basel, Switzerland)
Human action recognition has a wide range of applications, including Ambient Intelligence systems and user assistance. Starting from the recognized actions performed by the user, a better human-computer interaction can be achieved, and improved assis...

A Deep Sequence Learning Framework for Action Recognition in Small-Scale Depth Video Dataset.

Sensors (Basel, Switzerland)
Depth video sequence-based deep models for recognizing human actions are scarce compared to RGB and skeleton video sequences-based models. This scarcity limits the research advancements based on depth data, as training deep models with small-scale da...

Memory Attention Networks for Skeleton-Based Action Recognition.

IEEE transactions on neural networks and learning systems
Skeleton-based action recognition has been extensively studied, but it remains an unsolved problem because of the complex variations of skeleton joints in 3-D spatiotemporal space. To handle this issue, we propose a newly temporal-then-spatial recali...

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition.

Sensors (Basel, Switzerland)
Falls pose a great danger to social development, especially to the elderly population. When a fall occurs, the body's center of gravity moves from a high position to a low position, and the magnitude of change varies among body parts. Most existing f...

Locomotion of an untethered, worm-inspired soft robot driven by a shape-memory alloy skeleton.

Scientific reports
Soft, worm-like robots show promise in complex and constrained environments due to their robust, yet simple movement patterns. Although many such robots have been developed, they either rely on tethered power supplies and complex designs or cannot mo...